Proving motion planning infeasibility is an important part of a complete motion planner. Common approaches for high-dimensional motion planning are only probabilistically complete. Previously, we presented an algorithm to construct infeasibility proofs by applying machine learning to sampled configurations from a bidirectional sampling-based planner. In this work, we prove that the learned manifold converges to an infeasibility proof exponentially. Combining prior approaches for sampling-based planning and our converging infeasibility proofs, we propose the term asymptotic completeness to describe the property of returning a plan or infeasibility proof in the limit. We compare the empirical convergence of different sampling strategies to validate our analysis.


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    Titel :

    Exponential Convergence of Infeasibility Proofs for Kinematic Motion Planning


    Weitere Titelangaben:

    Springer Proceedings in Advanced Robotics


    Beteiligte:
    LaValle, Steven M. (Herausgeber:in) / O’Kane, Jason M. (Herausgeber:in) / Otte, Michael (Herausgeber:in) / Sadigh, Dorsa (Herausgeber:in) / Tokekar, Pratap (Herausgeber:in) / Li, Sihui (Autor:in) / Dantam, Neil T. (Autor:in)

    Kongress:

    International Workshop on the Algorithmic Foundations of Robotics ; 2022 ; , MD, USA June 22, 2022 - June 24, 2022



    Erscheinungsdatum :

    2022-12-15


    Format / Umfang :

    18 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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